Ricerc@Sapienza

With the advent of gravitational wave astronomy, the characterization of the source properties is a key task for the astrophysical community. The accurate theoretical modeling of the waveforms emitted by different sources is fundamental in this respect. The aim of our project is to introduce techniques from the field of deep learning into gravitational waveform modeling. In particular, we propose to exploit specific neural network architectures known as variational autoencoders: they are generative models, meaning that they learn to reconstruct and reproduce complex patterns inside observed fiducial data. The main goal is to use numerical relativity waveforms as a benchmark for an autoencoder to produce novel approximate numerical waveforms, without the need of running actual numerical simulations. The key motivation comes from the fact that numerical simulations are extremely costly, both in terms of time and computational resources. Neural networks are known to approximate with high efficiency arbitrary nonlinear correlations: therefore, we expect that a deep learning based approach to waveform modeling will be able to match and eventually exceed the accuracy of the current waveform approximants. Moreover, neural networks reconstruct correlations virtually without any external input: this diminishes the risk of biases and systematics, which might come from enforcing an a priori physically informed waveform structure. The proposed project is cross-disciplinary, as it lies at the interplay of deep learning and gravitational waves: this is an expanding research area, and we wish to actively contribute to it in the development of our project.
(NOTE: When citing bibliographical items, we will refer to their arxiv number: for example, [2005.03745] refers to the paper at the web link "https://arxiv.org/abs/2005.03745".)